title: GPU technology as a platform for accelerating local complexity analysis of protein sequences.
abstract: The use of GPGPU programming paradigm (running CUDA-enabled algorithms on GPU cards) in Bioinformatics showed promising results [1]. As such a similar approach can be used to speedup other algorithms such as CAST, a popular tool used for masking low-complexity regions (LCRs) in protein sequences [2] with increased sensitivity. We developed and implemented a CUDA-enabled version (GPU_CAST) of the multi-threaded version of CAST software first presented in [3] and optimized in [4]. The proposed software implementation uses the nVIDIA CUDA libraries and the GPGPU programming paradigm to take advantage of the inherent parallel characteristics of the CAST algorithm to execute the calculations on the GPU card of the host computer system. The GPU-based implementation presented in this work, is compared against the multi-threaded, multi-core optimized version of CAST [4] and yielded speedups of 5x-10x for large protein sequence datasets.
authors.forename: Agathoklis
authors.initials: A
authors.lastname: Papadopoulos
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kirmitzoglou
authors.forename: Vasilis J
authors.initials: VJ
authors.lastname: Promponas
authors.forename: Theocharis
authors.initials: T
authors.lastname: Theocharides
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein; D006193:Haemophilus influenzae / Q000378:metabolism; D010963:Plasmodium falciparum / Q000378:metabolism; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics; D012984:Software
chemical_list: D011506:Proteins